Development of text data analysis based on statistical modeling in medical big data
| Project/Area Number |
17K00047
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Statistical science
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| Research Institution | Shiga University |
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Principal Investigator |
IZUMI Shizue 滋賀大学, データサイエンス学系, 教授 (70344413)
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| Co-Investigator(Kenkyū-buntansha) |
佐藤 健一 滋賀大学, データサイエンス学部, 教授 (30284219)
冨田 哲治 県立広島大学, 地域創生学部, 教授 (60346533)
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| Project Period (FY) |
2017-04-01 – 2023-03-31
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| Project Status |
Completed (Fiscal Year 2022)
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| Budget Amount *help |
¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2019: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2018: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Fiscal Year 2017: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
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| Keywords | 生物統計 / データサイエンス / 共変量効果 / 出現頻度 / 可視化 |
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| Outline of Final Research Achievements |
Izumi, Tonda, Kawano, and Satoh (2017a, 2017b) used a semiparametric varying coefficients model to infer the effect of covariates on the probability and frequency of occurrence of keywords at the point of observation and visualized the relationship between changes in text features over time and covariates. This research was highly acclaimed and received the Best Paper Award. And Tomita, Sato, & Izumi (Hiroshima Med., 2018) verified the validity and features of the proposed method. And Obata & Izumi (2022) and Izumi & Sato (2023) applied the proposed method of this study to data in fields such as medicine.
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| Academic Significance and Societal Importance of the Research Achievements |
医療ビッグデータの内、経時的に観測されたテキストデータに着目し、性別や検査値のような共変量と併せて解析する方法を提案することを試みる。医療ビッグデータに加えて、他分野のビッグデータへの適用も考える。これにより提案方法の有用性を評価・検討できることは、学術的意義がある。たとえば、復旧・復興支援制度データベース（経済産業省）に蓄積されるビッグテキストデータの解析においても応用でき、情報処理のコストや労力が大幅に節減できる意味で社会的に意義深い。
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Report
(7 results)
Research Products
(87 results)
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[Journal Article] Contribution of radiation education to anxiety reduction among Fukushima Daiichi Nuclear Power Plant workers: a cross sectional study using a text mining method2021
Author(s)
Ryuji Okazaki, Kenichi Satoh, Arifumi Hasegawa, Naoki Matsuda, Takaaki Kato, Reiko Kanda, Yoshiya Shimada, Takuya Hayashi, Masaoki Kohzaki, Kosuke Mafune, Koji Mori
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Journal Title
J Radiation Res
Volume: 63
Issue: 1
Pages: 44-50
DOI
Related Report
Peer Reviewed / Open Access
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